A Hierarchical LSTM-Based Indoor Geomagnetic Localization Algorithm
文献类型：期刊论文
| 作者 | Wang, Liying1; Luo, Haiyong1; Wang, Qu2; Shao, Wenhua3,4; Zhao, Fang5 |
| 刊名 | IEEE SENSORS JOURNAL
 |
| 出版日期 | 2022-01-15 |
| 卷号 | 22期号:2页码:1227-1237 |
| ISSN号 | 1530-437X |
| 关键词 | Location awareness Sensors Fingerprint recognition Smart phones Wireless communication Buildings Wireless fidelity Location-based service indoor geomagnetic positioning data augmentation hierarchical LSTM network |
| DOI | 10.1109/JSEN.2021.3126731 |
| 英文摘要 | The traditional wireless signals used for positioning, such as Wi-Fi and Bluetooth, are not stable enough for accurate indoor positioning due to the wireless signal multipath and time-varying effect. Compared with the wireless signals, the geomagnetic field signals inside buildings are influenced by ferromagnetic materials, which are significantly more stable for accurate indoor positioning. However, due to the location ambiguity problem, different positions may have similar geomagnetic readings, leading to significant positioning errors. Existing indoor geomagnetic positioning methods generally rely on single or short-sequence geomagnetic observations, which makes it difficult to discriminate between positions with similar geomagnetic values. Besides, geomagnetic anomalies can provide accurate position estimation within small-scale areas, but they cannot be utilized for large-scale localization due to the extensive existence of contour points and their rarity of remarkable geomagnetic anomalies. Therefore, we presents a geomagnetic indoor positioning algorithm based on two-level hierarchical LSTM (HLSTM) neural networks, which significantly increases the positioning accuracy by incorporating more historical geomagnetic observations. Based on the finding that geomagnetic signals are much stable, we adopt a sequence augmentation approach to generate a large number of geomagnetic trajectories for model training and testing instead of labor-extensive human collection. The HLSTM model is trained by the Pytorch framework with Cuda and cuDNN for parallel. Our proposed algorithm can obtain around 0.8m error of 67% probability on the self-collected ICT dataset and public MagPIE dataset. The results of the comparison experiment demonstrate that our proposed algorithm performs better accuracy and robustness when compared with other algorithms. |
| 资助项目 | National Key Research and Development Program[2018YFB0505200] ; Action Plan Project of the Beijing University of Posts and Telecommunications by the Fundamental Research Funds for the Central Universities[2019XD-A06] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[62002026] ; Joint Research Fund for Beijing Natural Science Foundation and Haidian Original Innovation[L192004] ; Beijing Natural Science Foundation[4212024] ; Key Research and Development Project from Hebei Province[19210404D] ; Key Research and Development Project from Hebei Province[21310102D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000742197300020 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18213]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Liying |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China 3.Beijing Univ Posts & Telecommun, Natl Pilot Software Engn Sch, Sch Comp Sci, Beijing 100876, Peoples R China 4.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China 5.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Liying,Luo, Haiyong,Wang, Qu,et al. A Hierarchical LSTM-Based Indoor Geomagnetic Localization Algorithm[J]. IEEE SENSORS JOURNAL,2022,22(2):1227-1237. |
| APA | Wang, Liying,Luo, Haiyong,Wang, Qu,Shao, Wenhua,&Zhao, Fang.(2022).A Hierarchical LSTM-Based Indoor Geomagnetic Localization Algorithm.IEEE SENSORS JOURNAL,22(2),1227-1237. |
| MLA | Wang, Liying,et al."A Hierarchical LSTM-Based Indoor Geomagnetic Localization Algorithm".IEEE SENSORS JOURNAL 22.2(2022):1227-1237. |
入库方式： OAI收割
来源：计算技术研究所
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